检索结果-南通市图书馆

A Sensitive Wavebands Identification System for Smart Farming

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computer Systems Science & Engineering 2022年第10期43卷 245-257页

作者： m.kavitha m.Sujaritha Department of Computer Science and Engineering Sri Krishna College of Engineering and TechnologyCoimbatore641025India

Sensing the content of macronutrients in the agricultural soil is an essential task in precision *** helps the farmers in the optimal use of *** reduces the cost of food production and also the negative environmentalimpacts on atmosphere and water bodies due to indiscriminate dosageof *** traditional chemical-based laboratory soil analysis methodsdo not serve the purpose as they are hardly suitable for site specific soil ***,the spectral range used in the chemical-based laboratory soil analysismay be of 350-2500 nm,which leads to redundancy and *** sensors based on the discovery of spectral wavebands that respondto soil macronutrient concentrations,on the other hand,is an innovative and successfultechnology since the results are dependable and *** goal of thisarticle is to use a supervised neuro-fuzzy based dimensionality reduction approachin the sensor development process to determine sensitive wavebands of ***,the spectral signatures of the soil are collected inan outdoor environment and mapped with its macronutrient *** thisspectral analysis,the spectral reflectance of 424 wavelengths has been obtainedand these wavelengths are evaluated through combined and individual modesas *** wavelengths are selected in each case by minimizing the fuzzy reflectance assessment *** effectiveness of these selected wavelengthsin each mode is validated by modeling the relation between the reduced reflectancespace and each macronutrient concentration using Partial Least Squares multi Variable Regression(PLS-mVR)*** of optimal wavebands areidentified and the results are compared with the existing systems.

关键词： Sensitive waveband determination macronutrients feuro-fuzzy based dimensionality reduction partial least squares multi variable regression reflectance

Fuzzy Based Reliable Load Balanced Routing Approach for Ad hoc Sensor Networks

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computer Systems Science & Engineering 2022年第6期41卷 861-874页

作者： J.V.Anchitaalagammai Rajesh Verma m.kavitha A.R.Revathi S.R.Preethi Kiranmai Bellam Department of Computer Science and Engineering Velammal College of Engineering and TechnologyMadurai625009TamilNaduIndia Department of Electrical Engineering King Khalid UniversityAbha62529Saudi Arabia Department of Electronics and Communication Engineering K.Ramakrishnan College of TechnologyTiruchirappalli621112Tamil NaduIndia Department of Information Technology SRM Valliammai Engineering CollegeChennai603203Tamil NaduIndia Department of Electronics and Communication Engineering SRM Valliammai Engineering CollegeChennai603203Tamil NaduIndia Department of Computer Science Prairie View A&M UniversityPrairie ViewTXUnited State

Energy management and packet delivery rate are the important factors in ad hoc *** is the major network where nodes share the information without *** to the mobility of nodes,maximum energy is spent on transmission of *** energy is wasted on packet dropping and false route *** this research work,Fuzzy Based Reliable Load Balanced Routing Approach(RLRA)is proposed to provide high energy efficiency and more network lifetime using optimal multicast route discovery *** contains three *** first phase,optimal multicast route discovery is initiated to resolve the link *** second phase,the link quality is estimated and set to threshold value to meet the requirements of high energy *** third phase,energy model is shown to obtain total energy of network after transmission of packets.A multicast routing is established Based on path reliability and fault tolerant calculation is done and integrated with multicast *** routes can withstand the malicious *** decision model is integrated with propose protocol to decide the performance of network *** network simulation tool is used for evaluating the RLRA with existing schemes and performance of RLRA is good compared to others.

关键词： Fuzzy model link quality multicast route energy model cluster group node lifetime

Intelligent machine Learning with metaheuristics Based Sentiment Analysis and Classification

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computer Systems Science & Engineering 2023年第1期44卷 235-247页

作者： R.Bhaskaran S.Saravanan m.kavitha C.Jeyalakshmi Seifedine Kadry Hafiz Tayyab Rauf Reem Alkhammash Department of Information Technology PSNA College of Engineering and TechnologyDindigul624622India Department of Electronics and Communication Engineering K.Ramakrishnan College of TechnologyTrichy621112India Department of Electronics and Communication Engineering K.Ramakrishnan College of TechnologyTiruchirappalli621112India Deparmtent of Applied Data Science Noroff University CollegeKristiansandNorway Department of Computer Science Faculty of Engineering&InformaticsUniversity of BradfordBradfordUnited Kingdom English Department University CollegeTarabaTaif UniversityTaif21944Saudi Arabia

Sentiment Analysis(SA)is one of the subfields in Natural Language Processing(NLP)which focuses on identification and extraction of opinions that exist in the text provided across reviews,social media,blogs,news,and so *** has the ability to handle the drastically-increasing unstructured text by transform-ing them into structured data with the help of NLP and open source *** current research work designs a novel modified Red Deer Algorithm(mRDA)Extreme Learning machine Sparse Autoencoder(ELmSAE)model for SA and *** proposed mRDA-ELmSAE technique initially performs pre-processing to transform the data into a compatible ***,TF-IDF vec-torizer is employed in the extraction of features while ELmSAE model is applied in the classification of ***,optimal parameter tuning is done for ELmSAE model using mRDA technique.A wide range of simulation analyses was carried out and results from comparative analysis establish the enhanced effi-ciency of mRDA-ELmSAE technique against other recent techniques.

关键词： Sentiment analysis data classification machine learning red deer algorithm extreme learning machine natural language processing

Amodified Search and Rescue Optimization Based Node Localization Technique inWSN

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computers, materials & Continua 2022年第1期70卷 1229-1245页

作者： Suma Sira Jacob K.muthumayil m.kavitha Lijo Jacob Varghese m.Ilayaraja Irina V.Pustokhina Denis A.Pustokhin Department of Computer Science and Engineering Christian College of Engineering and TechnologyOddanchatram624619India Department of Information Technology PSNA College of Engineering and TechnologyDindigul624622India Department of Electronics and Communication Engineering K.Ramakrishnan College of TechnologyTiruchirappalli621112India Department of Electrical and Electronics Engineering Christian College of Engineering and TechnologyOddanchatram624619India School of Computing Kalasalingam Academy of Research and EducationKrishnankoil626128India Department of Entrepreneurship and Logistics Plekhanov Russian University of EconomicsMoscow117997Russia Department of Logistics State University of ManagementMoscow109542Russia

Wireless sensor network(WSN)is an emerging technology which find useful in several application areas such as healthcare,environmentalmonitoring,border surveillance,*** issues that exist in the designing of WSN are node localization,coverage,energy efficiency,security,and so *** spite of the issues,node localization is considered an important issue,which intends to calculate the coordinate points of unknown nodes with the assistance of *** efficiency of the WSN can be considerably influenced by the node localization ***,this paper presents a modified search and rescue optimization based node localization technique(mSRONLT)*** major aim of themSRO-NLT technique is to determine the positioning of the unknown nodes in *** the traditional search and rescue optimization(SRO)algorithm suffers from the local optima problemwith an increase in number of iterations,mSRO algorithm is developed by the incorporation of chaotic maps to improvise the diversity of the *** application of the concept of chaotic map to the characteristics of the traditional SRO algorithm helps to achieve better exploration ability of the mSRO *** order to validate the effective node localization performance of the mSRO-NLT algorithm,a set of simulations were performed to highlight the supremacy of the presented model.A detailed comparative results analysis showcased the betterment of the mSRO-NLT technique over the other compared methods in terms of different measures.

关键词： Node localization WSN chaotic map search and rescue optimization algorithm localization error

Segmentation of Brain Tumor magnetic Resonance Images Using a Teaching-Learning Optimization Algorithm

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computers, materials & Continua 2021年第9期68卷 4191-4203页

作者： J.Jayanthi m.kavitha T.Jayasankar A.Sagai Francis Britto N.B.Prakash mohamed Yacin Sikkandar C.Bharathiraja Department of Computer Science and Engineering Sona College of TechnologySalem636005TamilnaduIndia Department of Electronics and Communication Engineering K.Ramakrishnan College of TechnologyTrichy621112India Department of Electronics and Communication Engineering University College of EngineeringAnna UniversityTiruchirappalli620024India Department of mechanical Engineering Rohini College of Engineering and TechnologyPalkulam629401India Department of Electrical and Electronics Engineering National Engineering CollegeKovilpatti628503India Department of medical Equipment Technology College of Applied Medical SciencesMajmaah UniversityAlMajmaah11952Kingdom of Saudi Arabia Department of Electrical and Electronics Engineering SRM Institute of Science and TechnologyChennai603203India

Image recognition is considered to be the pre-eminent paradigm for the automatic detection of tumor diseases in this *** various cancers identified so far,glioma,a type of brain tumor,is one of the deadliest cancers,and it remains challenging to the medicinal *** only consoling factor is that the survival rate of the patient is increased by remarkable percentage with the early diagnosis of the *** diagnosis is attempted to be accomplished with the changes observed in the images of suspected parts of the brain captured in specific interval of *** the captured image,the affected part of the brain is analyzed using magnetic resonance imaging(mRI)*** of different modalities in the captured mRI image demands the best automated model for the easy identification of malignant *** of image processing techniques are available for processing the images to identify the affected *** study concentrates and proposes to improve early diagnosis of glioma using a preprocessing boosted teaching and learning optimization(P-BTLBO)algorithm that automatically segments a brain tumor in an given mRI *** involves contrast enhancement and skull stripping procedures through contrast limited adaptive histogram equalization *** traditional TLBO algorithm that works with the perspective of teacher and the student is here improved by using a boosting *** results obtained using this P-BTLBO algorithm is compared on different benchmark images for the validation of its *** experimental findings show that P-BTLBO algorithm approach outperforms other existing algorithms of its kind.

关键词： Brain tumor TLBO algorithm skull stripping preprocessing segmentation

Clustering Gene Expression Data Through modified Agglomerative m-CURE Hierarchical Algorithm

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computer Systems Science & Engineering 2022年第6期41卷 1027-1041页

作者： E.kavitha R.Tamilarasan N.Poonguzhali m.K.Jayanthi Kannan A Constituent College of Anna University University College of EngineeringVillupuram605103India A Constituent College of Anna University University College of EngineeringPattukkottai614701India Department of Computer Science and Engineering Manakula Vinayagar Institute of TechnologyPuducherry605107India Department of Computer Science Engineering Faculty of Engineering and TechnologyJAIN(Deemed-To-Be University)Bangalore562112India

Gene expression refers to the process in which the gene information isused in the functional gene product synthesis. They basically encode the proteinswhich in turn dictate the functionality of the cell. The first step in gene expressionstudy involves the clustering usage. This is due to the reason that biological networks are very complex and the genes volume increases the comprehending challenges along with the data interpretation which itself inhibit vagueness, noise andimprecision. For a biological system to function, the essential cellular moleculesmust interact with its surrounding including RNA, DNA, metabolites and proteins. Clustering methods will help to expose the structures and the patterns inthe original data for taking further decisions. The traditional clustering techniquesinvolve hierarchical, model based, partitioning, density based, grid based and softclustering methods. Though many of these methods provide a reliable output inclustering, they fail to incorporate huge data of gene expressions. Also, thereare statistical issues along with choosing the right method and the choice of dissimilarity matrix when dealing with gene expression data. We propose to use amodified clustering algorithm using representatives (m-CURE) in this workwhich is more robust to outliers as compared to K-means clustering and also ableto find clusters with size variances.

关键词： Clustering gene identifiers representatives dimension reduction

Hybrid Convolutional Neural Network and Long Short-Term memory Approach for Facial Expression Recognition

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Intelligent Automation & Soft Computing 2023年第1期35卷 689-704页

作者： m.N.kavitha A.RajivKannan Department of Computer Science&Engineering Kongu Engineering CollegePerunduraiErode638060Tamil NaduIndia Department of Computer Science&Engineering K.S.R.College of EngineeringTiruchengode637215Tamil NaduIndia

Facial Expression Recognition(FER)has been an importantfield of research for several *** of emotional characteristics is crucial to FERs,but is complex to process as they have significant intra-class *** characteristics have not been completely explored in static *** studies used Convolution Neural Networks(CNNs)based on transfer learning and hyperparameter optimizations for static facial emotional *** Swarm Optimizations(PSOs)have also been used for tuning ***,these methods achieve about 92 percent in terms of *** existing algorithms have issues with FER accuracy and ***,the overall FER performance is degraded *** address this issue,this work proposes a combination of CNNs and Long Short-Term memories(LSTms)called the HCNN-LSTms(Hybrid CNNs and LSTms)approach for *** work is evaluated on the benchmark dataset,Facial Expression Recog Image Ver(FERC).Viola-Jones(VJ)algorithms recognize faces from preprocessed images followed by HCNN-LSTms feature extractions and FER ***,the success rate of Deep Learning Techniques(DLTs)has increased with hyperparameter tunings like epochs,batch sizes,initial learning rates,regularization parameters,shuffling types,and *** proposed work uses Improved Weight based Whale Optimization Algorithms(IWWOAs)to select near-optimal settings for these parameters using bestfitness *** experi-mentalfindings demonstrated that the proposed HCNN-LSTms system outper-forms the existing methods.

关键词： Facial expression recognition Gaussianfilter hyperparameter optimization improved weight-based whale optimization algorithm deep learning(DL)

Propagation of an electromagnetic soliton in an anisotropic biquadratic ferromagnetic medium

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Chinese Physics B 2013年第3期22卷 275-281页

作者： L.kavitha m.Saravanan D.Gopi Department of Physics Periyar University The Abdus Salam International Centre for Theoretical Physics TriesteItaly Center for Nanoscience and Nanotechnology Periyar University Department of Chemistry Periyar University

Information storage technology based on anisotropic ferromagnets with sufficiently high magneto-optical effects has received much attention in recent ***-optical recording combines the merits of magnetic and optical *** investigate the magneto-optical effects on a biquadratic ferromagnet and show that the dynamics of the system are governed by a perturbed nonlinear Schro¨dinger *** evolutions of amplitude and velocity of the soliton are found to be time independent,thereby admitting the lossless propagation of the electromagnetic soliton in the medium,which may have potential applications in soliton based optical communication *** also exploit the role of perturbation,which has a significant impact on the propagation of an electromagnetic soliton.

关键词： solitons classical spin models maxwell equations nonlinear dynamics

Student Behavior modeling for an E-Learning System Offering Personalized Learning Experiences

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Computer Systems Science & Engineering 2022年第3期40卷 1127-1144页

作者： K.Abhirami m.K.kavitha Devi Department of Computer Science and Engineering Kings College of EngineeringPudukkottai613303India Department of Computer Science and Engineering Thiagarajar College of EngineeringMadurai625015India

With the advent of computing and communication technologies,it has become possible for a learner to expand his or her knowledge irrespective of the place and ***-based learning promotes active and independent *** scale e-learning platforms revolutionized the concept of studying and it also paved the way for innovative and effective teaching-learning *** digital learning improves the quality of teaching and also promotes educational ***,the challenges in e-learning platforms include dissimilarities in learner’s ability and needs,lack of student motivation towards learning activities and provision for adaptive learning *** quality of learning can be enhanced by analyzing the online learner’s behavioral characteristics and their application of intelligent instructional *** is not possible to identify the difficulties faced during the process through evaluation after the completion of e-learning *** is thus essential for an e-learning system to include component offering adaptive control of learning and maintain user’s interest *** this research work,a framework is proposed to analyze the behavior of online learners and motivate the students towards the learning process accordingly so as to increase the rate of learner’s objective *** to the demands of e-learner,an intelligent model is presented in this study for e-learning system that apply supervised machine learning *** adaptive e-learning system suits every category of learner,improves the learner’s performance and paves way for offering personalized learning experiences.

关键词： Learner behavior modeling e-learning intelligent learning system machine learning algorithm